CN115953268A - Hotel data processing system based on big data - Google Patents
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Abstract
The invention provides a hotel data processing system based on big data, which comprises an input/output module, a state recording module, a pricing module, a storage module and an analysis processing module, wherein the input/output module is used for outputting bookable room information and receiving the booking information of a user, the state recording module is used for recording the booking information of rooms, the pricing module is used for carrying out floatability adjustment on the prices of the rooms, the storage module is used for storing historical check-in information of all the rooms, and the analysis processing module is used for analyzing the check-in probability of the rooms. The system can adjust the price of the room based on historical check-in data and current real-time booking data, and improve the probability of booking the room by the user.
Description
Technical Field
The invention relates to the field of electric digital data processing, in particular to a hotel data processing system based on big data.
Background
The hotel is the most common living mode when going out for travel or going on a business trip at present, the hotel usually adjusts the price within a reasonable range to attract the user to come in, but the existing hotel system simply adjusts the room price according to the weak and prosperous season, but the adjustment mode is too simple, and the rooms of different types are not adjusted in a targeted manner, so that the better effect on the action of attracting the user is not achieved, and a more detailed price adjustment scheme is needed to help the hotel to better attract the user.
The foregoing discussion of the background art is intended only to facilitate an understanding of the present invention. This discussion is not an acknowledgement or admission that any of the material referred to is part of the common general knowledge.
Now a number of hotel data processing systems have been developed, and after a lot of search and reference, it is found that existing data processing systems are as disclosed in CN105354317B, which generally include loading in parallel a first state and a second state of each hotel in N comparison tasks in the date, and regarding the first state and the second state of each hotel in the N comparison tasks in the date as one-room comparison data; and judging whether the room state comparison data are different from the room state comparison data stored in a database in parallel, and updating the room state comparison data with the changed states in the database in parallel when the first state changes and/or the second state changes. However, the system is only used for updating hotel data, and does not utilize the hotel data to reasonably adjust the price, so that the application of the hotel data is still to be improved.
Disclosure of Invention
The invention aims to provide a hotel data processing system based on big data aiming at the existing defects.
The invention adopts the following technical scheme:
a hotel data processing system based on big data comprises an input/output module, a state recording module, a pricing module, a storage module and an analysis processing module;
the system comprises an input/output module, a state recording module, a pricing module, a storage module and an analysis processing module, wherein the input/output module is used for outputting room information which can be reserved and receiving the reservation information of a user, the state recording module is used for recording the reservation information of rooms, the pricing module is used for carrying out floatability adjustment on the prices of the rooms, the storage module is used for storing historical check-in information of all rooms, and the analysis processing module is used for analyzing the check-in probability of the rooms;
the analysis processing module comprises a vector processor, a vector register and a calculation processor, the vector processor is used for converting the data acquired from the state recording module into vectors, the vector register divides the vectors into historical comparison data, historical material daily data and real-time material daily data and then stores the historical comparison data, the historical material daily data and the real-time material daily data in a classified mode, and the vector processor is used for calculating elements in the vectors;
the analysis processing module calculates similarity based on historical comparison data and historical material day data, and then calculates estimated check-in quantity by combining with real-time material day data, and the pricing module calculates room price based on the estimated check-in quantity;
further, the status recording module includes a Room data register, a Room data reader, a user information register, and a Room number type register, where the Room data register is used to store all the Room data, the Room data reader is used to modify and read the Room data, the user information register is used to store user information of a reserved Room, the Room number type register is used to store a mapping relationship between a Room number and a type number, and the form of the Room data is Room (type, day, nmax, nbook), where type represents a Room type number, day represents a date, nmax represents a number of corresponding Room types, and Nbook represents a reserved number of corresponding Room types;
further, the amount of the rom data stored in the rom data register is Nd × Ntp, wherein Nd is the number of days reserved in advance, ntp is the number of types of rooms, the rom data register deletes Ntp data as date for yesterday every day, and adds new Ntp data as date for latest date capable of being reserved;
further, the vector stored in the vector register is represented by X day Represents, vector X day The elements in (A) are as follows:
X day ＝(f 0 ，f 1 ，···，f n ) day ；
wherein f is 0 Denotes the occupancy rate of all rooms, f 1 、f 2 、...、f n Respectively representing the check-in rate of the corresponding room types, wherein n is the number of the room types;
the analysis processing module calculates the similarity P according to the following formula:
wherein day 0 Earliest date, m, representing material day 1 Days of historical material, X1 day For vector data in history comparison data, X2 day Vector data representing historical material days;
the analysis processing module calculates the estimated probability value Q of the ith room type on the analysis day according to the following formula i ：
Wherein, X3 day Vector data of real-time material days;
the analysis processing module calculates the estimated check-in number N of the ith room type on the analysis day according to the following formula i ：
N i ＝Q i ·Nmax(i)；
Where Nmax (i) represents the number of ith room type;
further, the pricing module calculates a room price Pr for sending to the user according to the following formula:
where Pmin is the minimum value of the room price interval, pmax is the maximum value of the room price interval, and Nbook (i) represents the reserved number of room types in the i-th room.
The beneficial effects obtained by the invention are as follows:
the system stores historical data of the hotel, analyzes the historical data and current real-time booking data, calculates the final number of rooms entering the hotel on a specific date, and calculates the adjusted price of the rooms on the corresponding date according to the calculated number and the current booking number, so that residents can be attracted by price reduction when the expected number is low, the price is increased when the expected number is high, and the profit is increased.
For a better understanding of the features and technical content of the present invention, reference is made to the following detailed description of the invention and accompanying drawings, which are provided for purposes of illustration and description only and are not intended to limit the invention.
Drawings
FIG. 1 is a schematic view of the overall structural framework of the present invention;
FIG. 2 is a schematic diagram of a status recording module according to the present invention;
FIG. 3 is a schematic diagram of an input/output module according to the present invention;
FIG. 4 is a schematic diagram of the input/output module according to the present invention;
FIG. 5 is a schematic diagram of the analysis processing module according to the present invention.
Detailed Description
The following is a description of embodiments of the present invention with reference to specific embodiments, and those skilled in the art will understand the advantages and effects of the present invention from the disclosure of the present specification. The invention is capable of other and different embodiments and its several details are capable of modifications and various changes in detail without departing from the spirit and scope of the present invention. The drawings of the present invention are for illustrative purposes only and are not intended to be drawn to scale. The following embodiments will further explain the related art of the present invention in detail, but the disclosure is not intended to limit the scope of the present invention.
The first embodiment.
The embodiment provides a hotel data processing system based on big data, which is combined with fig. 1 and comprises an input and output module, a state recording module, a pricing module, a storage module and an analysis processing module;
the system comprises an input/output module, a state recording module, a pricing module, a storage module and an analysis processing module, wherein the input/output module is used for outputting room information which can be reserved and receiving the reservation information of a user, the state recording module is used for recording the reservation information of rooms, the pricing module is used for carrying out floatability adjustment on the prices of the rooms, the storage module is used for storing historical check-in information of all rooms, and the analysis processing module is used for analyzing the check-in probability of the rooms;
with reference to fig. 5, the analysis processing module includes a vector processor, a vector register, and a calculation processor, the vector processor is configured to convert the data obtained from the state recording module into a vector, the vector register classifies and stores the vector after dividing the vector into historical comparison data, historical material daily data, and real-time material daily data, and the vector processor is configured to perform calculation processing on elements in the vector;
the analysis processing module calculates similarity based on historical comparison data and historical material day data, and then calculates estimated check-in quantity by combining with real-time material day data, and the pricing module calculates room price based on the estimated check-in quantity;
the state recording module comprises a Room data register, a Room data reader-writer, a user information register and a Room number type register, wherein the Room data register is used for storing all the Room data, the Room data reader-writer is used for modifying and reading the Room data, the user information register is used for storing user information of a reserved Room, the Room number type register is used for storing the mapping relation between the Room number and the type number, and the Room data is in the form of Room (type, day, nmax and Nboost), wherein the type represents the Room type number, day represents the date, nmax represents the number of the corresponding Room type, and Nboost represents the reserved number of the corresponding Room type;
the Room data register stores Room data, wherein the Room data register stores Room data, and the Room data register stores Room data, wherein the Room data register stores Room data, and the Room data register stores Room data;
x for the vector held in the vector register day Represents, vector X day The elements in (A) are as follows:
X day ＝(f 0 ，f 1 ，···，f n ) day ；
wherein f is 0 Denotes the occupancy rate of all rooms, f 1 、f 2 、...、f n Respectively representing the check-in rate of the corresponding room types, wherein n is the number of the room types;
the analysis processing module calculates the similarity P according to the following formula:
wherein day 0 Earliest date, m, representing material day 1 Days of historical material, X1 day For vector data in history comparison data, X2 day Vector data representing historical material days;
the analysis processing module calculates the estimated probability value Q of the ith room type on the analysis day according to the following formula i ：
Wherein, X3 day Vector data of real-time material days;
the analysis processing module calculates the estimated check-in quantity N of the ith room type on the analysis day according to the formula i ：
N i ＝Q i ·Nmax(i)；
Where Nmax (i) represents the number of ith room type;
the pricing module calculates a room price Pr for sending to the user according to the following formula:
where Pmin is the minimum value of the room price interval, pmax is the maximum value of the room price interval, and Nbook (i) represents the reserved number of room types in the i-th room.
Example two.
The embodiment includes all contents in the first embodiment, and provides a hotel data processing system based on big data, which comprises an input and output module, a state recording module, a pricing module, a storage module and an analysis processing module;
the system comprises an input/output module, a state recording module, a pricing module, a storage module and an analysis processing module, wherein the input/output module is used for outputting room information which can be reserved and receiving the reservation information of a user, the state recording module is used for recording the reservation information of rooms, the pricing module is used for floatably adjusting the prices of the rooms, the storage module is used for storing historical check-in information of all rooms, and the analysis processing module is used for analyzing the check-in probability of the rooms according to the historical check-in information;
the state recording module divides the Room into at least two categories and assigns a type number to the Room of each category, and the state recording module represents the booking information of the Room through Room (type, day, nmax, nboost), wherein the Room is a data type, the type represents the Room type number, day represents the date, nmax represents the number of corresponding Room types, and Nboost represents the booking number of corresponding Room types;
with reference to fig. 2, the status recording module includes a Room data register, a Room data reader/writer, a user information register, and a Room number type register, where the Room data register is used to store all the Room data, the Room data reader/writer is used to modify and read the Room data, the user information register is used to store the user information of the Room in advance, and the Room number type register is used to store the mapping relationship between the Room number and the type number;
the Room data register stores the Room data, wherein Nd is the number of days reserved in advance, ntp is the number of types of rooms, the Room data register deletes Ntp Room data with day as yesterday and adds Ntp Room data with day as the latest date which can be reserved;
the Room data reader-writer can read the Nmax value and the Nboot value in the Room data and calculate a difference value, when the difference value is not 0, the corresponding Room type is sent to the input-output module, when the Room data reader-writer receives Room ordering information from the input-output module, the corresponding Room data is retrieved in the Room register according to the type value and the day value, and the Nboot value of the Room data is added with 1;
the number of the user information stored in the user information register is the same as the sum of Nboost in all the Room data, the user information comprises identity information and Room information, the identity information is obtained from the input and output module, the Room information is type when in a reserved state, and after a user actually enters the Room, the Room information is converted into a specific Room number according to the type and the Room number type register;
with reference to fig. 3, the input/output module includes an application processing unit and a status reading unit, the application processing unit is configured to process a room ordering application of a user, the status reading unit obtains room information from the status recording module according to room ordering application information, and with reference to fig. 4, the workflow of the input/output module includes the following steps:
s1, the application processing unit receives a booking application of a user;
s2, the application processing unit acquires date information from the booking application and sends the date information to the state reading unit;
s3, the state reading unit sends a command containing date information to the state recording module to obtain room information, and sends the room information to the application processing unit;
s4, the application processing unit sends the room information to a user corresponding to the room booking application;
s5, the application processing unit receives the determined booking information;
s6, the application processing unit sends the user information and the room type information in the booking information to the state recording module;
it should be noted that the booking application only contains the date, and the booking information includes the specific contents of the room type, the date of check-in, the user information, etc.;
when the from data register deletes a group of from data, the from data register will backup the group of from data and send it to the storage module, the storage module will sort the received from data, the data form stored by the storage module is a vector with day as unit, and X is used day Represents, vector X day The elements in (A) are as follows:
X day ＝(f 0 ，f 1 ，···，f n ) day ；
wherein f is 0 Denotes the occupancy rate of all rooms, f 1 、f 2 、...、f n Respectively representing the check-in rate of the corresponding room types, wherein n is the number of the room types;
the analysis processing module acquires historical data from the storage module, acquires current booking data from the state recording module, and calculates pre-estimated check-in data of each room type within a bookable date based on the historical data and the booking data;
the workflow of the analysis processing module comprises the following steps:
s21, selecting an analysis day from the current bookable dates;
s22, taking m days before the analysis date as material days, wherein the number of days of the material days is the same as the number of days of the bookable date and is m days;
s23, acquiring data of the material day of the previous year from the storage module;
s24, dividing the material date into a historical material date and a real-time material date, wherein the historical material date is a material date not belonging to the bookable date, the real-time material date is a material date belonging to the bookable date, acquiring the historical material date data of the year from the storage module, acquiring the data of the real-time material date from the state recording module, and converting the data into a vector X3 day ；
S25, the analysis processing module calculates the similarity P according to the following formula:
wherein day 0 Earliest date, m, representing material day 1 Days of historical material, X1 day Vector data representing material day of the last year, X2 day Vector data representing historical material days;
s26, the analysis processing module calculates the estimated probability value Q of the ith room type on the analysis day according to the following formula i ：
S27, the analysis processing module calculates the estimated check-in number N of the ith room type on the analysis day according to the following formula i ：
N i ＝Q i ·Nmax(i)；
Where Nmax (i) represents the number of ith room type;
the analysis processing module sends the estimated check-in quantity of the analysis day to the pricing module, the pricing module adjusts the price of each type of room according to the estimated check-in quantity, the pricing module is provided with a corresponding price interval [ Pmin, pmax ] for each type of room, and the pricing module calculates the room price Pr for sending to the user according to the following formula:
the pricing module sends room prices to the input-output module, where the room prices are included in the room information in step S4.
The above disclosure is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, so that all the modifications and equivalents of the technical changes and equivalents made by the disclosure and drawings are included in the scope of the present invention, and the elements thereof may be updated as the technology develops.
Claims (5)
1. A hotel data processing system based on big data is characterized by comprising an input and output module, a state recording module, a pricing module, a storage module and an analysis processing module;
the system comprises an input/output module, a state recording module, a pricing module, a storage module and an analysis processing module, wherein the input/output module is used for outputting room information which can be reserved and receiving the room reservation information of a user, the state recording module is used for recording the reservation information of rooms, the pricing module is used for floatably adjusting the prices of the rooms, the storage module is used for storing historical check-in information of all rooms, and the analysis processing module is used for analyzing the check-in probability of the rooms;
the analysis processing module comprises a vector processor, a vector register and a calculation processor, the vector processor is used for converting the data acquired from the state recording module into vectors, the vector register divides the vectors into historical comparison data, historical material daily data and real-time material daily data and then stores the historical comparison data, the historical material daily data and the real-time material daily data in a classified mode, and the vector processor is used for calculating elements in the vectors;
the analysis processing module calculates similarity based on historical comparison data and historical material day data, calculates estimated check-in quantity by combining with real-time material day data, and the pricing module calculates room price based on the estimated check-in quantity.
2. The big-data-based hotel data processing system as claimed in claim 1, wherein the status recording module comprises a Room data register for storing all the Room data, a Room data reader/writer for modifying and reading the Room data, a user information register for storing user information of the reserved rooms, a Room number type register for storing a mapping relationship between the Room number and the type number, and a Room data in the form of Room (type, day, nmax, nbook), wherein type represents the Room type number, day represents the date, nmax represents the number of the corresponding Room type, and Nbook represents the number of the reserved rooms.
3. The big-data based hotel data processing system of claim 2, wherein the amount of Room data stored in the Room data register is Nd × Ntp, wherein Nd is a number of days booked in advance and Ntp is a number of Room types, and wherein the Room data register deletes Ntp times per day of Room data by day of day by day, and adds new Ntp times by day to the Room data by the latest date that can be booked.
4. The big-data based hotel data processing system of claim 3, wherein the vector stored in the vector register is represented by X day Is expressed as vector X day The elements in (A) are as follows:
X day ＝(f 0 ，f 1 ，···，f n ) day ；
wherein f is 0 Denotes the occupancy rate of all rooms, f 1 、f 2 、...、f n Respectively representing the check-in rate of the corresponding room types, wherein n is the number of the room types;
the analysis processing module calculates the similarity P according to the following formula:
wherein day 0 Earliest date, m, representing material day 1 Days of historical material, X1 day For vector data in history comparison data, X2 day Vector data representing historical material days;
the analysis processing module calculates the estimated probability value Q of the ith room type on the analysis day according to the following formula i ：
Wherein, X3 day Vector data of real-time material days;
the analysis processing module calculates the estimated check-in number N of the ith room type on the analysis day according to the following formula i ：
N i ＝Q i ·Nmax(i)；
Where Nmax (i) represents the number of ith room type.
5. The big-data based hotel data processing system of claim 4, wherein the pricing module calculates a room price Pr for sending to the user according to the following equation:
where Pmin is the minimum value of the room price interval, pmax is the maximum value of the room price interval, and Nbook (i) represents the reserved number of room types in the i-th room.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5864818A (en) * | 1993-01-04 | 1999-01-26 | Feldman; Ron | Automated hotel reservation processing method and system |
| US20150248707A1 (en) * | 2014-03-03 | 2015-09-03 | Adara, Inc. | Hotel Recommendation Engine Based on Customer Data from Multiple Online Sources |
| CN105005579A (en) * | 2015-05-28 | 2015-10-28 | 携程计算机技术（上海）有限公司 | Personalized sorting method and system of hotel room types in OTA (Online Travel Agent) website |
| CN109376891A (en) * | 2018-11-27 | 2019-02-22 | 携程计算机技术（上海）有限公司 | The determination method and system of the full room state of people place |
| CN109544406A (en) * | 2018-11-02 | 2019-03-29 | 北京联合大学 | A kind of multi-functional Hospitality management system |
| KR20190041439A (en) * | 2019-03-19 | 2019-04-22 | 주식회사 공감랩 | System of building big data for estimating house price using space information having function of data error correction |
| CN109685234A (en) * | 2018-12-27 | 2019-04-26 | 携程计算机技术（上海）有限公司 | The processing method and system subscribed for hotel's house type |
| CN110634020A (en) * | 2019-09-06 | 2019-12-31 | 北京美住美宿科技有限公司 | Price adjusting method for hotel industry market |
| CN112529333A (en) * | 2020-12-25 | 2021-03-19 | 上海云角信息技术有限公司 | Prediction method, device, equipment and storage medium for overdesigned number of hotel rooms |
| CN112734457A (en) * | 2020-12-25 | 2021-04-30 | 上海云角信息技术有限公司 | Hotel guest room dynamic pricing method, device, equipment and storage medium |
| CN114358838A (en) * | 2021-12-31 | 2022-04-15 | 上海珞瑾数据信息有限公司 | Self-adaptive hotel room time-sharing price adjustment system and method |
| CN114445138A (en) * | 2022-01-27 | 2022-05-06 | 深圳市天下房仓科技有限公司 | Hotel room type pricing method, device, equipment and storage medium |
-
2023
- 2023-01-04 CN CN202310005173.1A patent/CN115953268B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5864818A (en) * | 1993-01-04 | 1999-01-26 | Feldman; Ron | Automated hotel reservation processing method and system |
| US20150248707A1 (en) * | 2014-03-03 | 2015-09-03 | Adara, Inc. | Hotel Recommendation Engine Based on Customer Data from Multiple Online Sources |
| CN105005579A (en) * | 2015-05-28 | 2015-10-28 | 携程计算机技术（上海）有限公司 | Personalized sorting method and system of hotel room types in OTA (Online Travel Agent) website |
| CN109544406A (en) * | 2018-11-02 | 2019-03-29 | 北京联合大学 | A kind of multi-functional Hospitality management system |
| CN109376891A (en) * | 2018-11-27 | 2019-02-22 | 携程计算机技术（上海）有限公司 | The determination method and system of the full room state of people place |
| CN109685234A (en) * | 2018-12-27 | 2019-04-26 | 携程计算机技术（上海）有限公司 | The processing method and system subscribed for hotel's house type |
| KR20190041439A (en) * | 2019-03-19 | 2019-04-22 | 주식회사 공감랩 | System of building big data for estimating house price using space information having function of data error correction |
| CN110634020A (en) * | 2019-09-06 | 2019-12-31 | 北京美住美宿科技有限公司 | Price adjusting method for hotel industry market |
| CN112529333A (en) * | 2020-12-25 | 2021-03-19 | 上海云角信息技术有限公司 | Prediction method, device, equipment and storage medium for overdesigned number of hotel rooms |
| CN112734457A (en) * | 2020-12-25 | 2021-04-30 | 上海云角信息技术有限公司 | Hotel guest room dynamic pricing method, device, equipment and storage medium |
| CN114358838A (en) * | 2021-12-31 | 2022-04-15 | 上海珞瑾数据信息有限公司 | Self-adaptive hotel room time-sharing price adjustment system and method |
| CN114445138A (en) * | 2022-01-27 | 2022-05-06 | 深圳市天下房仓科技有限公司 | Hotel room type pricing method, device, equipment and storage medium |
Non-Patent Citations (5)
| Title |
|---|
| SEUNG-HUN, LEE: ""Analyzing Determinants of Hotel Room Price Using the Hedonic Price Model: Focusing on the Operational Data and OTA Data of Hotel Industry"", 《JOURNAL OF TOURISM AND LEISURE RESEARCH》, vol. 30, no. 2, 31 March 2018 (2018-03-31) * |
| 吴越强等: "《康养休闲旅游服务系列教材 康养旅游住宿服务》", 31 August 2021, 北京：旅游教育出版社, pages: 243 - 252 * |
| 王菁;: "酒店管理***设计", 《办公自动化》, no. 04, 15 February 2010 (2010-02-15) * |
| 葛佳丽;: "互联网下在线价格影响因素研究――以邮轮船票为例", 《电子商务》, no. 09, 10 September 2020 (2020-09-10) * |
| 青十五: "《策略产品经理：模型与方法论》", 30 September 2020, 机械工业出版社, pages: 140 - 145 * |
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